{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document and Auto-completion in Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# np.[TAB]\n",
    "np."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Help document\n",
    "np.random.normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# [Sh-Tab] to show function arguments\n",
    "np.random.normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector & Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6]\n",
      "1\n",
      "(7,)\n",
      "0 1 6\n",
      "[2 3 4] [4 5]\n",
      "[0 1] [3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "t = np.array([0, 1, 2, 3, 4, 5, 6])\n",
    "\n",
    "print(t)\n",
    "print(t.ndim) # rank\n",
    "print(t.shape) # shape\n",
    "print(t[0], t[1], t[-1])\n",
    "print(t[2:5], t[4:-1])\n",
    "print(t[:2], t[3:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 5 2] \n",
      " [[ 1.   0.7 -0.2]\n",
      " [ 0.2  1.   0.5]\n",
      " [ 0.2 -0.5  1. ]]\n"
     ]
    }
   ],
   "source": [
    "vec_x = np.array([2, 5, 2])\n",
    "mat_A = np.array([[1, 0.7, -0.2], [0.2, 1, 0.5], [0.2, -0.5, 1]])\n",
    "print(vec_x, '\\n', mat_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4 25  4] \n",
      "\n",
      "[[1.   0.49 0.04]\n",
      " [0.04 1.   0.25]\n",
      " [0.04 0.25 1.  ]] \n",
      "\n",
      "[[ 2.   3.5 -0.4]\n",
      " [ 0.4  5.   1. ]\n",
      " [ 0.4 -2.5  2. ]] \n",
      "\n",
      "[[ 2.   3.5 -0.4]\n",
      " [ 0.4  5.   1. ]\n",
      " [ 0.4 -2.5  2. ]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Vector * Vector (Matrix * Matrix): Component-wise\n",
    "print(vec_x * vec_x, '\\n')\n",
    "print(mat_A * mat_A, '\\n')\n",
    "\n",
    "# Matrix x Vector ??: component-wise in each vector\n",
    "print( mat_A * vec_x, '\\n')\n",
    "print( vec_x * mat_A, '\\n') # Same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "# Dot(inner) product\n",
    "print( np.dot(vec_x, vec_x) )\n",
    "print( vec_x.dot(vec_x) ) # same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.1   1.5  -0.05]\n",
      " [ 0.5   0.89  0.96]\n",
      " [ 0.3  -0.86  0.71]] \n",
      "\n",
      "[ 5.1  6.4 -0.1] \n",
      "\n",
      "[3.4 5.4 4.1]\n"
     ]
    }
   ],
   "source": [
    "# Matrix multiplication ?\n",
    "print(mat_A @ mat_A, '\\n')\n",
    "\n",
    "print(np.matmul(mat_A, vec_x), '\\n')  # sig_v is treated as a column vector\n",
    "\n",
    "print(np.matmul(vec_x, mat_A)) # sig_v is treated as a row vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2]\n",
      " [5]\n",
      " [2]]\n",
      "[[2]\n",
      " [5]\n",
      " [2]]\n",
      "[[2]\n",
      " [5]\n",
      " [2]]\n"
     ]
    }
   ],
   "source": [
    "# Convert vecto to a matrix with single column\n",
    "print( vec_x[:,None])\n",
    "print( np.reshape(vec_x,(3,1)) )\n",
    "print( vec_x.reshape((3,1)) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0]\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "vec_y = np.ones(10)\n",
    "vec_z = np.zeros((5,), dtype=int)\n",
    "print(vec_z)\n",
    "\n",
    "#mat_B = np.zeros((3,3))\n",
    "#mat_B = np.zeros_like(mat_A)\n",
    "mat_B = np.ones_like(mat_A)\n",
    "print(mat_B)\n",
    "\n",
    "x_norm = np.random.normal(10,2,size=(10000,5))\n",
    "x_unif = np.random.uniform(size=(10000,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations on Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Axis 0: Column, Axis 1: Row\n",
    "print( np.mean(x_norm, axis=1) )\n",
    "print( np.std(x_norm, axis=1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009315002671939333\n",
      "[ 0.02477672  0.00943336  0.02205258 -0.00783942 -0.00184823]\n",
      "[ 0.06884625  0.19151922 -0.36689168 ...  0.56107496 -0.10636838\n",
      " -0.76635605]\n",
      "0.009315002671939333\n"
     ]
    }
   ],
   "source": [
    "print( np.mean(x_norm) )\n",
    "print( np.mean(x_norm,axis=0) )\n",
    "print( np.mean(x_norm,axis=1) )\n",
    "print( np.mean(x_norm,axis=(0,1)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -1   1   1 ...   1   1  -1]\n",
      " [  0   0   0 ...   0   2   0]\n",
      " [ -1  -1   1 ...  -1   1  -1]\n",
      " ...\n",
      " [ 32 -88 -22 ...  14 -12 -24]\n",
      " [ 33 -87 -21 ...  15 -13 -23]\n",
      " [ 34 -86 -22 ...  14 -14 -24]]\n"
     ]
    }
   ],
   "source": [
    "# Random Walk Simulation\n",
    "x_rw = np.random.binomial(1,0.5,size=(1000,10))*2 - 1\n",
    "x_cum = np.cumsum(x_rw, axis=0)\n",
    "print(x_cum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8f09a20>,\n",
       " <matplotlib.lines.Line2D at 0x8f09b70>,\n",
       " <matplotlib.lines.Line2D at 0x8f09cc0>,\n",
       " <matplotlib.lines.Line2D at 0x8f09e10>,\n",
       " <matplotlib.lines.Line2D at 0x8f09f60>,\n",
       " <matplotlib.lines.Line2D at 0x8f0e0f0>,\n",
       " <matplotlib.lines.Line2D at 0x8f0e240>,\n",
       " <matplotlib.lines.Line2D at 0x8f0e390>,\n",
       " <matplotlib.lines.Line2D at 0x8f0e4e0>,\n",
       " <matplotlib.lines.Line2D at 0x8f0e630>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JJWgSE1AGBhJ01WwA7HVmUEhqjaZ9H9MyfBbxfoPd56xr+IUwf1Al+hKbl8weYR0hsf3c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57neLEM862urebHgz+SW57L8xue52CBTOS7e/ndpBVLva3VGaupPEOkTJhRITTLyo+vGQwcXe7P\niY1BfP7gnSz75D3Ki4vwDbPV2IraYHvRKpi7n6pjRVQeKaR0c9Y5qZ6lDP7pEAKufBPd/du5c5LU\nnnn0hxT2ZRWjafDssc4c1CJ4ML6IWeOTcGik6FVypA8niyrNRt+aIK+GR6ZOTk4EBwdTVWXJ9mzf\nvn2D+5vPGRREWFgYo0aNMrddFu2CEIIp/dsybWAMvdv68cEHH5i37927l8DAQJvJ2fONh4PePCnY\n1dMNTwc914X40dXLjediI3iwjdTmsedHvpDRNM0m3h3AcJZp+QbNYD5mSeoSPk75mPkH53P777fb\n7GfK5UjJSaHWUGv3dTRNo9ZQi6ZpdPV049pg3wb7o2kaO3N20i+sHz7OPqTkpNjdb+CEWHR6wZFN\neQQUyRFveqAndy38gP0FB6jVatmTt8fusfaY9Osk5uyfw1U/XoWmaSxPW85/1v2Hod8NZc7+OYxb\nMK7eZwqwN2/vaT9bv4hIHF1cOXFwP5pB7pd7/BhFxzwpzZT5PH8t/ZXF772JT1QyumrZ5lc2Atci\n6a71GGx711zw3X4K5h+kbKt9YcfmpEkGXwjxqhBinxBipxDiRyGEj9W2x4QQh4QQ+4UQVzS9q+eX\nrlH1R957M4tIc41Hd+h3eMYXClIbda5kYyTPNe+utWn3dj3zpKLJxWJ6joyMZObMmXb96dOnT0ev\n15v31el03HfffQDc2cP2/SxYYJtQcvLkScLD7WvSnE8Wde9A1tBkfutRX7K3u7f8c/Xw+nsVkVor\nT6x9gv7fWjTi159YT59v+pBdlt2o448XHSdpdhJJs5PQNI2jRUfN21KLUm32NRnkZWnLSP4qmaTZ\nSSw/ZpssdPvS20n+KpnE2Yk8tPIh/kj7gwHfDqCwsn4gwg2LbuB48XESAxNJCEgwX1Dq4uCox9Uo\nmKezssNHY+6k0HeGTd8aQ065dK3UarV8uutTs1vJmvELx9dru/m3m0mancTmrM31tgHodHp8QkL5\n6/dFvHHD1VRXVZJ5sH4VtMxDB9A7udD58Gzifv+C9t5PEPHSQCJeGojPKFvXpKFM3t3X5J19wZ+z\npakj/KVAF03TEoEDwGMAQohOwESgMzASeE/YUy26gAiuM/L2NarhlQdZxc2mrmnUueJDbZNSPrip\nO3Om9+G3+wee8ViTEU5MTGTSpElmNw3AHXfcgW+SLyUOJTg5ORESEsLkyZNtwiF9fX1xc3Njw4YN\nvPbaa2bXjclN1KaNJYLHw+PCKOpgYpifJ190acvlAecnfrs5eWnTS9y25Daqa6tZcHgBxdXFfLn7\nS+bum8sbW9+gvKacrSe3NupcG7MsFcDSi9PZlSvDsjydPNELPRU1Ut65sraSfQX78HG2HQw8sPIB\niqqKqKip4JXNr7Ax03K+ZWnLWJ2xmuLqYhujWlZdxsubXmZ3nsyJiPePJyEwgcOnDlNSZV92QG+U\n3DjYYR1L2keb2ys8euHskWTjDjpQWsErRzPtjtLrnn/Wtll2LzT78qWswgPdHuD3637Hy8nyu/n1\n6K92+wjgYFVYPvvoETIP7sfV04vp731B//E3oXd0pLyokC8enoE+VEae1S3TGPJoT7xHt7Wpme3a\n+cy1eZtKkwy+pmm/a5pmCijfAJjuVcYCczRNq9Q07ShwCOhl7xwXEo+PjqdPjB/juoVzWbx0H3i2\n72vZYfdPYDjzrbazg57rusmPamxyGCO7hNAnxp8wnzOHJcbGxtKuXTvi4uKIjY3FyerHFxoaysB+\nAznofRC39m7odDqioqJsDLcQgvDwcHJzcykpKeGTTz4hO9syUhw/fjxJSXKyqVevC+sr0wnByEDv\nVilRcDbkV+Tzv73/Y3PWZuYdmGduf23Lazy38TmzodqctZkd2TvYm7f3tOcz7Q/w9d6vWZOxhnGx\n43i+//PUarUsS1vG7tzdrElfQ42hhus7XF/vHC9sfIElqUv4as9X9bb9cPAHAL7b/515TuDHQz/y\n9d6vzft0C+pGYkAiGhqLU6VstEEzkFaUZt5nxG2dOB6wB++eNSRF+uBgZcvT/f7JzpydHC6rQNM0\nvszI5Y3UkzZhuCZMF5l/9/43AA46B5vPaFrCNJv9b+p0E6EeoTbRQ7ll9bWCzP2cdrd5eceShRze\nsoHQ2Dg8/QPoc91Ehk25A4C89DSOFqfg0skfpwhbg+/g44LnoAhcOsi7fY9B4fUuCi1Bc/rwbwN+\nMy6HA9aiKOlYApNsEEJMF0JsEUJsycnJsbdLq+H2QTHMmd6XN8Yn0yfGHwedILaL1cTMoaWw5o1G\nnev18UmkvjSGWRPPLrPOw8ODm2++GW9v+yn+8f7xHPE6wqfln3K86LjdfepG3rz3nsy6nThxIu7u\n7lx77bXMnDkTL68Lf6R8IWLtunhx04sN7jfvwDxu/u1mxi8cf9rRfkpuCt2NUuHf7JPhfwkBCSQE\nygS8x1Y/xsRFE3lgpRT1Mhn8kdEjGRI5BIBFRxax8MhC8zk7+VtChk0+71Xpqxjx/QgMmoETJZYo\nlm/HfIuLgwtdAroA8PT6p8ktz+XDnR8y5scxZqNfGVDIotgPSQiW+00Kty2+c7zWl/4b9zEnK59t\nRgnmbXa0902j+dFtR/NYr8eoMdRQZahiRJsR6ISOa9tbqrB9NeornPUyjPLqdrK04cDwgezM3Wn3\n7gEgsE1bHp4rP4t9a1dRUVpCaHuLPlNIe4u7saAyi4BbOiEc7Jtap7byf+wc0/gqZk3hjAZfCLFM\nCLHLzmOs1T6PAzXA/0xNdk5l99PTNO0jTdN6aJrWw168d2tlXNdwVvxzCGF+nvDQPjD+SPnjWSiw\nXxDbLulb4ZsJUF1x5n0bgauD5S5hedpypv0+jZyyHGZtm8WcfVK/u6HPuTX67C81Xtj4Avf8cU+9\n9qf7PW2zXjeW3VpWw5rPd33Onrw9JAfaynl3CehCgGt9F4KDcCDcI5xl1y/juQHP8drg18zbNmRu\nMC9/evmn/Dz2Z7uvOfansczeM9u8HucrjaG3s2WQsit3F3+kySzUMT+Oobq2mit/vBKQFyOA52Ij\neCNVT8+D8r9RGPw4AA/uO8724oYN/s6cnbTxaoO3szeJgRa1z0d6PMLCaxcS5RXFH//4gx+u/oHk\nIMvncn+3+1ly3RIGRwwmvyKfE6W2arj/3HeckBU7mPSXbcjq5sT+/BJskVMJio5hyuvv4RcWwckj\np5d8dgp158MBv9Jrw2Byyxu+q2guzmjwNU0brmlaFzuPnwGEEJOBK4FJmuWSmA5Ya71GAGcW0r6A\n0OkEkX7GaBCvUJhgdav78VBoYHRgw5GV8MkwOLC4nhBbU5g1dBYAr299nY2ZGxk2bxifpHzC8xuf\nhz0L6BAbS//+/Zkwwbbqladny99SKuyz7eQ21p9Yz7f7vjW3eTrJ72NIxBCuirkKJ50TLnoXpnSe\nwuO9H7c5fm++fbfOG1vlHWfngM58fPnHAFwRfQUdfOUotO6FxESwezDOemec9c58dsVn5vY43zge\n6/UYHk4exPjEMCNpBt2DuzPnyjnmfUwTwZ38O/Fknydx1FuCEV4a+BIgjbJ1Jq3JzQPQ3kdGnjnq\nBNFernQ+Zr/MopMQbCu0lWAwRQWZLhpxvnE46ZwIdA0kxD2ESM9IvjqRy/YyJw7UBNqM4h10DoR5\nhJnvfEwX0WW5+dy0dQNfG6W7l+cXUVxTy6Tn38DRxZWV/UbxabUTh8ssgzb/iCja9OrHWuFMVWXD\ng7my6jJ+ylsIAn469FOD+zUXTY3SGQn8H3C1pmnWl9oFwEQhhLMQoi0QC2xqymu1evxiYKjxT1iW\nB3sXnH7/mkqYPdayfmJ7w/ueJcOihjW4rWTeLTgc/JURI0bYhHTGxcU1eIyiZamurWby4slMXzrd\n3HZ1u6u5r6uMqHp50Ms46h3pH96f0TGjebjHwwwMlxP8bwx5g+FRw+1GsBRVWVQduwZ1pU9oH1Im\np/Da4NfQGSXDx8WOI9Q9lBFtRgDwQPcH6p2nZ0hPs2vn5k43c2P8jeZtdyXfxRcjv6Czf2f+usU2\nEubWzrcyPs42EmZMzBji/eJZe2KtOf4d4Ju90tXk4+yDXmeJ72jfI4jQAvvKmdcE+7CntJxyqypc\nmaWZ5FXkmUf2jnpH+oX3Y1DEIIQQbC4s5ZH96dyScpSpu1JZmldU77yxvrG46F3YmbOT8loDN6Wk\nsazINmhjR1EZIe07cMPHlgzZ/htttfVTouP5ccQNLN/f8CjfNN8AkFduv150c9JUaYV3AGdgqTHN\neIOmaXdqmrZbCPEdsAfp6rlbqyu8fjEy+F+w4nm5fGg5fHeLXB76BDh7wIoX4YGd8Go76Gkb/8zv\nT0BET6nc2QwkBibavc3f7exE7xwZRubo6Njs8giKs6f717YqnT9c/QOxvrIy1MSOlsIybw+ziNwF\nuweTMlka+ePFx1mWtoxpS6bxyRWfmPcxReN8OOJDu+4bE79f//sZ+5gUmMTK4yttXCR10dWpO2Hy\n2dclMTCRufvnmvt2x9I72JUn+/rnhD9t9g1q48X97w1jUnoxc5/bzPL2s1lnvCgt2/UMNQH30vW7\n63mm20TGx403R/IkBsh+3rU7lVy/+/lvcnvKag1ctc22/OAtKTJE9Yfk9vTzlcENjjpHOvl3IiU3\nhV0ltqGSi7t3YOTWA2wrKmOgn2c9l1KtppmT/o64+0JFCWtOnGRUou1nsf5UCdduP4Sfrpb8KOkd\nGNup5QddTTL4mqY1mPWjadrzwPNNOf8FyX3b4e2usM0iFMWK5yzLf80BQw1sNGqEdB4Hu2WUA59d\nAY+fBMe/mexUXQFr3oR+9zBr6Cw+3/U5PYJ78MfxP2iTd5xZBVuZERzE+oytWAtAVNVW8WnKp9zU\n6SazG0FxbiirLkOzmt7qE9rHbOwbS2d/WcFtY9ZGtp7cyutbXuflgS+bR/0NGd6zYULcBCI8I2jr\nbb9yl4mfx/7MsaJj1Gq1RHjal+VICEhg7v65CITZ9WKiIRE+v1B3hKNGUEkbfDMfBeGGqJHRZYVB\nD/PMhrsprCzk7e3yomhyWf2YLZMbZ6We5IidiB4T43Yc4uigRBblnOKR/en08xrF9hO/88wBeYFw\nKt9BN7dqkr2SCXcy8PbhFLKOZJOZXYNDiKCvjwerC0o4UFpBvFEEcGeFvDNZYnCibXoO0yIsc2cm\n1dd8g+W//mtuIR0bISDYFFSmbXPjFwNJNzS8fdOHtuv/+BxumGtZ3/E/GqQkBwynuVHa8TWsegmW\nPE6AawCP9HyEoVFDebbv00wzTnJV6QTz8ndQVlVKYWUhWaVZrM1Yy3t/vcfvqWce6SmaF+vs0eTA\nZF4Z9MpZnyMp0KLZMmXxFFJyU7hvxX2k5KbQ1rutTXz538XTyZOR0SPPuF+MTwxDo4YyvI39ugeA\n2Ufe1rstnk6evDxQSpS8OujVBo/R6XX4RrgSVNIGh+oMHKoOojPIRC9N506N62De2foZaOBa5Ymj\n3pETFRbf/4tHM5mblQ/IkovXBfvaaDABvJGaxT170yg3GFhe1ZH8gPvYXAK6mly8c16nKP07istK\nyM1fS6kugM9FLSt9NaINel7uIKcstxpH/KU1tewvlb77dFdPnjiYweb8k2SVZpFRUcXx8vrzEnq7\nsS7Ni1LLbAkiesBf39rflm81w2+KqIkbCYHxkLO34Wzd8gKYlQiXPws9p9nfx3Tsti9lNa8IWaSB\nlS/CwSWER0eTIQy85OnEZz+OJrtC/gGGRAwBZDjbdR2uq39eRYthckGsmrAKP5e/V8TcxcGFb8d8\nyw2LLAON1MJU8srzGBQxqFn62ZxEe0Xj5+JHt2BZVnJ0zGhGx4w+43FR7QPJSS1Bb3CgVidHzz4O\nOk7VGDgVeCuebhPovutrBqZez6mry9gq7E/2vlyndvDlW/azs7ic/6bZz1x2qE4nMTCRfgum8va2\nX3Dsf5hK5BxKhZOO6DJo6+qEr4OebUWl3BTmz47iMgxA19pytuvl//yqvzLxPfF/FITJC1zkiaMc\nD5N3THt6d8DPTgGh5kaN8FuCcKOh7XEb3GhJnCHGmPHa6RqYNB8estIGmboEXP0anrxN3wrVZXD0\nzyS6F2kAACAASURBVPrbDLXw412wyeK/5ZPLLKGemz4C4Dt9DC46OaoxGXuAlekrAfvCVoqWJSUn\nhUjPyL9t7E2YolFM1Gq1FFQWnNbnfr7QCR1fj/6ah7o/dFbHhcR4o9cc+L/sd0nIHEznrAE8axWc\nVOzuRrv8brw/0pseW/dx++5UAPqnWRRj7Y2h5yfbeqZvC7ed7/DHiX4LpgLgUxaMQ6VtWKZLfhVC\nCLp6ufFNZj5rCorNvv0bA2xdpAWhlryKiMxUbvrhfW6d+zblaamN+ASajjL4LUFIAvS+Sz5iR0C/\n++CO1ZYJ2egBEDsc3Kz+5C7ekHA9ZGyz77YxhW2m20mwObkb/voGasrB3+rHm7Yeik5AhfRjel35\nJm29Y+ofj4y/PnzqMKXVZ1FpqKLQfn9akEMFhzhZ2vIiU+eKnbk76/mx/w6Oekfi/WVFr/u63mee\nF2iOc7cEkZ6RZz1fFBwt4/jzj1Yw8OQ1DM66nuxtufgWW/4vB9p1JtdbT4mrxbQN2FTMde4e3Bzm\nz4JuseSfKGXf+kyzOqWng57PrcoyPhsbTpCTdH54lK3n/uO2uk0+JYUMzK+gY1opLpXldNpWTG21\nwVxg5/odh9lWVEZbVyeu69iebrs34nPKeJEwTmx71VbTfec6QrMzCCjIZuNP3zWY6NWcKIPfEuj0\nMOolCOwgFTcvfxZCE2UlLZ0jtG3gNju8B1SXQradmOp0o8EvSoeiTNtt1jH8g/4l5xEAMv+CN4xl\n/UY8C77RTLCK+vDUOdE3VEpDjGgjMyR359qvB2uX7ybLPIKK+qJZLYGmadyx7A5e2vTSOXm9liar\nNIvssuxmG4X3D+tPuEe42T3ionc56wng1oyHr6XUpqFch6Fcjtd7HbTEua/uYDvpmZBaiVMt3F7k\nxKtxkfT0dufbZzay/Mu97F5jSQ0aFehD4Kka/Ipq0QvBED9PBvh4cGjMXfiU2cqABZdEcd0ewbVp\nJ0na8y7O5QZy00u4KtCSLbutqJRuXu64ubgwNnULo5ZZCrm4lBdzx8dP41pZzk8DZR+Obt/CgQ22\nYootgTL455LIXvBoGgQ2EH5l8rnXTcJa946UbQiMt7/depQd3h3u3gxe4bDsKUt7kEyFHxc7jo0V\nPnjWGkjQHEkKkhN+ptjqM7p18g7DTG/5OLLC2J9tpz+mmcgszSS7LJvt2dvPyWioJdE0jRHfy9j3\n5hqFT0+czoJrFhDmHkaAawCd/DvVy8i90Jnycv96bT0PVvKv+fl29oaZvv64eTtx8qiMty8ttETq\nrPpmPyUF8mKxbv4hbv+9iDsXF7J67gHeiIvim6QYWSP5RAntuwcx7c1B6B0F9wU+Rv6JUtzD9Zz0\nSAXg4P7j3LewP77FvwCyMptJprs62JXgAkfmdpJ39EH5FgmZgHBLEZmcYxYV05bi4vo1XAg4nWZi\nxi8GXHzgl/uh87XSzQPwuzGhq+MYObH7/VR49BjonaR//ugqCE2GXrdDgNGlE9UXdn0vl1185N0F\nMuzN7Ya5vPR+V4JqTxJ4WS9C+4WSFJhEG682tgk8Jdmw6CHodz9EGjWD/rJkVJpJ3wKnjsnb1eAu\nEN6tCR9Qw5guRnkVeWSWZhLmcW6Ls/xdNmVuIr0kHVcHV0a1lTUJjhVZ5Dc6+nVsltfR6/TokaPR\n5wc8j7eTfb2lCxl3b2d8gt0oyitHM4BfqBtDJnWkptrA4U1HmR8hx7BveQZQcrCQPtfHUJJTwaGt\n2XQfVUxRrm3W65ePrcM/3J28jFJM4/idK9IZOKEDICjKLae8uJrwDj44uzoQGOnFoc3ZaBoM7dGH\nz3drlDqeYsH6FIgBQ9kO8JQ1oH20LCCQTK8y/Gp1dKqo5akoD7K//A6AP7pl8+nlC3DsVc2C118g\nvGP9GtbNjTL4rQkhoMroQ1/4EFz9X7BKS6fbzbD6NaithPXvQkAsLH5Ubut6M3S9ybJveDeLwZ++\nAqzru3qFMShpKmx4Fz4bzbgn5YgjISCBjZkbLfU6f5oh7yz2/iLvTFy8oTCdYiEwCIGnwSBvEVPm\nQa6VJvgjR8DJHTTD6S9wZ4n1xWhn7s5mNfg1hhpqtVqzkJYZg0HOgTi6UVZbgauzt/1Y8ZpKQIBD\n/RKXU3+fal4eHDEYN0c3s8BXe5/2OOmbv3h1v7B+zX7O1kKHXsEU5VVgqDHgF+ZOSIy8sN2ZVcq2\n0lzu6xrJxFB/6CHzAALbeHJoazZzn9tMwpAIdHpB95Ft2LwoFYC8jPrzVhWl1bi4O5K+X5YiDTaK\nnAW39SLriHRhhsf48aLfi/ycshX/QvlbdC3PoEgzoDMIlm79jGtCX2evyKQ/gqzDBxnlH8D88hKW\nd8/miuGTCHEPAXe49Y1zU7tXuXRaG52kYh+7vocXQmHOJLk+7mPwjZYuG5Aj6hyrYt4RttmahBj9\nwsOetPj0rQk1bq+1hK4lBCSQU57DyTLjpGiOVar4S1GQsZXtWZvoFx3JgDYRvNOuGyTdaGvsAV6N\ngeeDZf+bSRQOZNhoZ//OOOudz6oYRmN4c+ubNmGNZhY/Cq+0Je/FUHrPHWgj+WvDf7vDR0PqNdct\nCmJKpd+ZsxM3Bze+v+r7pnb9kqPnmLZcdks8I27rTPeR0eb2hMERrB+dzA2htiqbgVbSxCkr03F0\n0dPrqvr/icundebqB6SYWnZqEfknSlnxlfwP+IXLojqmguvega64eDiSEJBAtscxvCsCca3y5PaN\nLxBSYCAsv4aCYyV8/uhqhm19COHoROpf25j/gixzmONTSdegs1PKbQ6UwW9tXP1f2/WDS+Szyb9/\n03xw9oJts83hloDlQmCi7UCY8isMeND+6yROgHaXyeU1b0FJNkkLHwNkeTjD3oVQWEdeefdPrK6y\n+B8/NuTWv9DUZe4k++1l+fIiMtMb3h8gw0rTNsIvD0BN/fjpakM1e/L20C24G/F+8fVkI344+AM/\nH7Kv3tgY1p1Yx8GCg/UVC42Jcruc5Sj8lc2vSM33rV/C034yr2Lnd/Kzyt5drx5C3cIbL2x8gafW\nPcWc/XPoEtDFRjfmrNnwgazBoDgtkZ38iO0ZbF53dJaf+S0v9GPQxA4MmRTH5dM60757kCyoLuBk\nahEnDllKkOqN5UtNBj84Rj77uviS7SHdc5O3yoz669aVcM2GUgbtvYHKklqEEDg4hnFgg6VA0ntX\nf2LWQzqXKIPf2nByh5vr/Ind/MHXmNLu6gvtjPH8pdngFiDDPl19658rur+MGLKHEDDAKJS17ClY\n/TodSuUPPLssm1W/TCNXr2NV39uocTS6ZVLmcaxO0fTcQGPImpMnpXGjOFq3kPWhZZC1C06lQe5B\naSALM2DZTEt0z8kUGVb62eWw9XPIqB/quSR1CZW1lSQGJJIQmMCOnB2kF6ebtz+17imeWPsEBRUF\n9t9vAxwoOMDu3N0cOiUFrnbm7GRT5iZWHl9JaWUxAMccHPjYx+IPf3vzq/DLfaDVwqJ/wg9Wukj5\nh82LaUVpfLxTKlQOihiEXug5dOqQuWDI6fRt6lFbDZlWFzlDLfzxHKx9q/6+NZWwf7HMzFYA0PVy\ny+ToqDvkJLmnnwsJQyLoPDCc2B7BCCFwcnXAL9SdPWtOkLJS/r4un9bZfKynnwtdBofTeYDFnTh1\nmO2gxrfUgE+Z7YXfwcny/xx44xR6hvRsUEaiJVE+/NZIu6EQPRBSV8v18O7SQJuIvxr2GEezQx9r\nOPP2TIRZ3VJm7sS6ou59wUbdj6xl3DbyXzx4cAvsW0iKTxgjIy+je1gfnt/4PENX3U2KWwD0vpMP\n3HXMNRxhzejvcJplSffng/qRFaclYwu0sVQSK68p57HV8u7DlJYPMOqHUaRMTrGp7Trsu2Fsv6Vx\nyqN55Xlct8A2s/jL3V+yLVtGHXX3jecz4MpI27mCU/sthUAoyrA9afoWCIhF0zTG/DjG3PzuZe9y\nz9cDWVVrGTUOjRpKo1k2E9a/A/duA/92kHsAqorlxbS6wlZ/6bf/kxdOgJnnJmS2teMfJl0yHfuE\nENTm9FITfmHuHNqSDQWVePq7ENvDcncghGDwDbZRduO7XM+7/GHTVuaXh1u+dC3lhhzFNy0KkL/L\nXmPrVxQ7V6gRfmvlpvkwWYZ4mTN3TSRcD2HGSJjQJvgBnT1hktGHnLYOXHzYkFq/StZnuz7jaFAs\nOXodmQ4OJAR3s6l4NKFjV052u5FtJ7dRXlPO/ppieOQwPHAGP3tosjRgdUm3LSBtrTcT5h5mY/T/\nu/2/XDbvMvN6jVbDL4d/sfty3+77loQvE1h5fCVQ390SZBBmYw+wtWAvSW0tI8OAmlr6lpdz0FS9\nyMXbMs9x9Tvg5GkOmZ2xfIb5OFPhj5BSy91HUmBSo7RpAMjZL409wH+7ydG96TMyVEOW1cg/K8Vi\n7KFxdRkuAXR6HVNfH8iQm88cEeXha7l4egc2Tszs9rcGMemZPkx7cxA3vNCdGU9ZpCLcOlehd2qH\ns/ftOPvcQ2W5lIXY8msqa78/2NApWwRl8FsrDs7QZgAMfxq6T66//YZv5bawJk78tB0kwzsB+t2L\ne8/beaikut5uVx+fz1/OMoIlMTCRLgFdaOfdDoA9BfuZufE5c9jkvAPzeHHXxxS5+chJXWCzizOv\n+flgc2bvCDlaHf+VOWwUBxebvIJtJ7cx/8B8AJ7s8yRCCMLcw+gd0huAj3Z+RF3+vebfdt/qCxtf\nAODeP+5lbcZa7v3jXvO2q6odcLMzd2DCx9mHz7JyGFhWQZaDAw8FBZDXX+rVr3Bz5WRUDwjvCumb\nqUnfypoM6a/1dPRk9qjZUJzFvSfTuafgFPeFX8ZbQ9+ClO9h9Rv2jfLJPZC6Vj7m3my77c9X4ddH\n5GcFthfI35+w3TffVgbgUsbF3dHsiz8dPUdHE9HRl+C2Xlw2uXGhkk4uDvgEueHs6oCfnzfuzu4Y\nrjnC0tgv6BAvtXuEzhMhnNi04Ah5J0rYs/YEe9acMGf8nguUwW/N6HTSz+4ZUn+bZ4jcpmviV+jg\nbInoiegJo19lygxLpu/ULpaQwhcDA3EQejr6dUQndLwx1FK/12TgQBaw/mbfNzy7/lkY8zoacFto\nMF96e/GVt6dFDLivsRh0p6th+EzwDJX6Q0XpUJyFpmlMXjyZX478QrhHuLmYhhCCVwfbKit6OXnx\n49U/mtcNmgGDZjAnaFXV2hrzO5fdaV52NRh4MuMoDxZId8ugKgNvnbT1f6+asIq2I18joVIm7ix1\nd+Phgk0U6nTcFxzInWv/DRE90bJ2cXD2FebjVk9cTYx3DKRvwdugccepIm6vdSdAOML8qbD8aUjb\nUN/oL34U5k2BL0bXj4Ja+SLUVMiRvleEJQtb06SvH6CP8bNNb75KapcKTq4OjH2gK9f/Xw+b7N6z\nZUj33hRHZdA3phf+xigfkHH+c57ZRHFeBVUVtRRklZ2zREJl8BUyA1jozXcLQghSJqeQMjmFB7o/\ngLuj/LFm68DXxQ8X48gy2iu63qmsk4j25e8DJzey/mkJ73zTz5exyYOlb7mNVax4aBI8vE8KywEr\nUmaTONsiOWDSfDfh6+LLW0MsE5Z/TviT9r7teabfM4Ac5SfNTuKxNY9Z+oKlMIaJ9p5t2HQsHVdN\nY1hZOSlH03g3I52ESnmBGCI8SZmcIot7dJ9M/L8sE8VbC/YyoI2M9T506hAzK1NJjA5nfHgoAL8e\nz0CfarwQZmyRshqeodI985LFVcTnI+FpHxmlBNKQZ2yTk/ImEsbDUxb/v+z8cBkllbFFZkA/7QPH\n1kK3yVLOw9G9WUtnKs6OhMAElv9jOUFuQUx8sjd3fzAMJ9f606bfPrOR9+5aQU5acYv3SRl8BQz8\np5wvcLE/mWVdrNo6jFAndHw75luz4Z8YN1GOZo2UVJfIGqN15BqOFh6tN+I2E5oIOgfmpC2xabYn\ntDUsahhP9H6C2aNmm/tlkilYdGSR+fnVza8y6VcZSWFdcu+62Ov4pK1VCb7YK2CwTGQLqq3lY/8B\nvHCNldop4Kx35tPLP8VFX79Izfwc27mHiJpamH21TKZL3wIhXeS8SUPs/kFOwv5wu5yQtcbNX07c\nT7CqlzDuQzm/cypNuodMeIbI6KywrjJ0N1OpoLYW9I4Wkyt0tlE6qSmtoIi54hLA3V+GcDZAsHsw\nj/WSI+W6RSq6BHThzSFvMjB8IA92f5B7ku+hf3h/7km+h9zyXDJLM9mZsxMnnRPDoyxFMUwj7no4\nukJIAmVWgmw9A5K4ve3VljDDPBn6KIRgQscJNgksbb2icXewnWibvWe2eXlk25G4OrgS7dmG/3Se\nhv9JYz/aDoIr35B1BAB829Lnyvfx9Aqv18Veob34T9//NPh5AdxYWGyR4v3jeSl7HdETrrUqgNN+\nBAx6xLKeth6+GAO75tuerO0g6HePXI6/EpInwQ1z5KRxhFHyYvtXlv17GN1wXsboog8HQq39urCK\nc8vI6V3wDZV3zAERHjbbTPkBLYky+IpGcWP8jaRMTiE5KLnetva+7Xlv+Hu4OboR6RXJB8M/YEDE\nAEBKIKTkptDJvxODIwebj6kbIWNNTVg39muWDN3PNv9C+IdD4bX2sO9XGamyd6HdY/XbvqBLsYyG\n6VFum+Xr4+SNs96ZTZM28UsR6N7sLCc8I/vIOxzvCPAOl5nJ0QNO+3lYC551C+rGPcn32Gx/LN8q\nH2DDu1BVIkfj4d2kO2tmIdz0PQx7Qi7HjZHqphVWbpu2g6DtYEvfTFzzHsRJTR5Ck6Q7rvC43Hdm\nIXgawwhNE+Eg5woU552w9j7c8GQvXL2cCIv1oWMfOT83fdZgkodHneHoptMsBl8I8U8hhCaECDCu\nCyHE20KIQ0KInUKIllHTUrRaTDVFH1n1CNuzt5MQmMDYdmP5ddyvBLkF2S2wbuKQfxTlOsHjufms\nOZZuu9GUeTx3Eix53Hbb70/AoodJNE6sPpJfQLyjDIm8P/8UvyX/n2VfU45D+iZLFrOJ25bAyNNL\nMEd7R/PbuN/44x9/8P7w982honcn383K8SulntDDdSZb676ONZ7B9dvGz4bxX9Zvt8bJDYI72z9/\n0kQIMCbG7ZxLo9k1HxY2kKGtaDJCJ5jweE96j41hyE0dmfxiv3MyuodmMPhCiEhgBJBm1TwKiDU+\npgPnRhlI0Wpw1DnarCcGJCKEINIzksSAxNOO8Hc6yWP7l1fgXUeqwJxwBnLyM/cQHF0NhemwTspS\njCsu4fZThcRVVfPO4T3cdqqQyYVFeKx4UU6IVtURywpNsl33CAJn29tte0R4RhDoFoiboxs9g3ty\na5dbub7D9fi7+ks3mWcI3Pid5QB7mkYmhlqFU3YYBVMWyexpexnU9TpiNPR18zWEgJuNkUtnI5O8\n9UvY8pk0/Oeo1sGlhru3M45OevQOOpu4/5amOUb4bwL/AqzjisYCszXJBsBHCBHaDK+luICYFG9J\nObdOlkoITOB48XHyrcosWpNSnoVvbS0RNXb8zuV1pBPe6Q5fXglvWqJ4Impquc+5DXrk5OuDBYUy\nizh7N2z+BE7ssD1Hm6YrSzrqHXmo+0P15RI6XCENcfRA22zpurj7wzUfyOWx757RpWT7GiOlBHZk\n7/rbvCNk8ZuijMZJLZgihAC+vw1+vrvx/VC0epokrSCEuBrI0DTtrzq6EOGAdcpmurGtTqkmxcXM\no70e5X97ZVRJmLtFnsAUGrkrdxc/HvyRZWnLABnz/0D3B0jJTSGhzVDEbe/anvCdnlJSYMwbMgLl\n4wakCUa+BH3ugpk+gAY+UTI+vThTZsZWl8v9HjkiDW1Lc/vyxu2XfIN8nC0drpD1ERrCNLGbscXi\n+69LTSW83FZOWltHCB3fdPb9UbRazmjwhRDLADuZPzwO/Bu43N5hdtrsZhYIIaYj3T5ERbX8pIXi\n3PLT2J8orCy0EYoyyTLcvdx29Pjprk+ZmjCVI4VHGNnWjuxAeA9p8CN6yGSx2CssPn0T17wvY9YB\n7tsOmz42Jnhp8i4gex+U5kgxunNh7FsDJpfVtxOlXHaXcRb30v7FMrEr77Asr7mxjve15CQcW9cs\nd0GK888ZDb6macPttQshEoC2gGl0HwFsE0L0Qo7oI612jwBO1DuJPP9HwEcAPXr0UMIfFxntfNrV\na3NzbLgoyvK05Who9RKkAKkhVJIFQZ2le2Tki7YGv+89kHyjZd2vLYx8wbLe+07pn3bxlvLRlwrW\nRWj+eBZ2/QAz1sn1byfYP+by52D5s7LYzuejlAjbRcLf9uFrmpaiaVqQpmnRmqZFI418N03TsoAF\nwC3GaJ0+QKGmacqdozBjLb4GmHV5nlz7JABdArvUP6j9ZXISUm8cp/jFgEewrPQ1sxCueP70Lxre\nA2rK5UWj7gTnxY71+zVJOJeeJtGn370w1epiaqhtmX4pziktJY/8KzAaOASUAbe20OsoLlA+vfxT\ntmVvw8PRA3dHdzycPBg53+LG8XI6vYQtIEf5ty0BV5/Gvah12KLJr32pcPOP8JLxpru2WhaZsaez\nM+l7qWIKcp6k370y+mnXfDi4VE4o2ynjqLgwaDaDbxzlm5Y1QE3vKxrEw8mDQRGDzOvW4lFP9zuL\nJCG/to3f1zdaShRUFkuZg0sJFy+ZnbvmLTi+QRadMensJE6UevoJ/6gfHdT9VmnwTUVeetxmU6tA\ncWGhCqAoWgVCCLoFdeNY0THGxY5rqReRZR3LcqVK6KVG3CgISZCT1+lbjfo+CVKTpyHq5g6c2K4M\n/gWMMviKVsMnV3zSQCxXM3LNe5d2URCTNtBvRg2f7mfwttbNHahTnEZxYaEMvqLVUDc7t0XQn4PX\naM3UNeCNmcu4c40s1FJ4XMktX+Ao8TSF4lLDWrHzdPo+JkIS4B+fQ6exUor5VP0ymIoLA2XwFYpL\njaSJ4NNGLvvHNv44U2jnW10ubbfYBYwy+ArFpci05XDXurMrkWktMrf/N/jtUTAYpIDdooct5RUV\nrRblw1coLkU8AuXjbHByk0JtBxbDHKPmj2cw7JwnhelCEqRYW0AHqV+kaHWIc1U8tzH06NFD27JF\nTQopFK2W2mp4MVJmLJ8OJcVwThFCbNU07YwTMsqlo1AoGo/eEcLqVz2rR2OkmBXnHGXwFQrF2RHe\nXT7HX2XVVmdwqcI3WyXK4CsUirMjbrRM4LryLbh1sWy7/jMIToCe02SNXZWg1SpRk7YKheLsiO4P\nD+2Ry+4BFn/9XWvkc/pm+8JsivOOGuErFIrmJbwHHF0lK5RlpcDif8Pix5RfvxWgRvgKhaJ5Ce8O\nWz6V1ck+sFLfPLYOJs2TReIV5wU1wlcoFM2LaVK3Lpk74LWzyOxVNDvK4CsUiuYlqCPM2AiBHe1v\nX/zYue2Pwowy+AqFovkJ6ijF1gBihsCAhyzbNrwHa9+G2ho4uRtyDpyPHl6SKIOvUChahvYjwNEN\nrpoFw5+Cf1uVtV76JGz8AN7vB+9eYuUmzyPK4CsUipYhsic8nilLS4LU4pm61LL998ctyzWV57Rr\nlypNNvhCiHuFEPuFELuFEK9YtT8mhDhk3HZFU19HoVBcBET0hOF2ahZn7Tr3fbkEaVJYphBiKDAW\nSNQ0rVIIEWRs7wRMBDoDYcAyIUQHTdNqm9phhUJxASMEDHgAXH3gl/st7Qd+g4gGonsUzUZTR/h3\nAS9pmlYJoGlatrF9LDBH07RKTdOOAoeAXk18LYVCcbHQfYrM0DVl6f75Khz+47x26VKgqQa/AzBQ\nCLFRCLFKCGGafQkHrOugpRvbFAqFwj5fXQuVJee7Fxc1ZzT4QohlQohddh5jkS4hX6AP8AjwnRBC\nAMLOqewK7wshpgshtgghtuTkqNRrheKSY8oiy/LBJeevH5cAZ/Tha5o2vKFtQoi7gB80WUVlkxDC\nAAQgR/SRVrtGACcaOP9HwEcgC6A0vusKheKiIHoARPWDtHWQve989+aipqkunZ+AYQBCiA6AE5AL\nLAAmCiGchRBtgVhgUxNfS6FQXKxM/kWWRlQ6+i1KUw3+Z0CMEGIXMAeYrEl2A98Be4DFwN0qQkeh\nUDSI3gGi+sqJ2/nToKrsfPfooqRJBl/TtCpN027SNK2LpmndNE37w2rb85qmtdM0LU7TtN+a3lWF\nQnFRE2GsmpUyD9a8eX77cpGiMm0VCkXrIKyrZflU2vnrx0WMMvgKhaJ1EJIAN8yR+jvZe853by5K\nlMFXKBSth7hR0OcuqaJZVQYFx6As/3z36qJBGXyFQtG6iOgJWq0smDIr0bZqlqJJKIOvUChaF+HG\nydvPR8nnogzYOe/89eciQhl8hULRuvAIBO8o27Yfpp2fvlxkKIOvUChaH26+9ds0lYjfVJTBVygU\nrQ9TIfTpK+Gqt+Vy3uHz1ZuLhibp4SsUCkWLcMUL0GGkjM3XO8m2jC0Q0P789usCR43wFQpF68PR\nFToYC+UFdgQnDzi2FgrTz2+/LnCUwVcoFK0bnV6O9LfNhjc7Q3X5+e7RBYsy+AqFovVj0tkByPzr\n/PWjLttmw0xveDHqgijErgy+QqFo/YR1syynW0kon0qDjK3nvj8mFtwrnysL4cT289ePRqIMvkKh\naP1E9rYsW2vmL34M/veP8xOyWXdEn7Ht3PfhLFEGX6FQtH48g2XB807XWEb4mgbpm6EsD/KPnPs+\nZe6Uz+O/ks9LHpPunVaMMvgKheLCIaInFB6Ht7tC2gYoOSnb089DpSzTnUZET9v2kuxz35dGogy+\nQqG4cDBN3uYfgc9HWtoPLLbv1tE0KM1tmb6kbwGvcPAKhQn/s21vpSiDr1AoLhxCk+q3eYXD7h9g\n3X/rb1v7FrzaDopONH9fMrZYMoLjr4THs0Dn0Krr8iqDr1AoLhwcXWHGBts2j2D5vPRJ28SsU2mw\nbKZcbkwkT3U5fD8Vcg+eed8/noOCVNtwUUdXCO4MGz+Cn+8GQ+sr460MvkKhuLAIioc718jl720g\nbQAAChRJREFUCV/DtR9atq18UT5rGnxm5fJpjJtl9Ruw63v47V+n30/T4M9X5XJd/31ET6gqhu1f\nQ87+M7/mOaZJBl8IkSyE2CCE2CGE2CKE6GVsF0KIt4UQh4QQO4UQ3c50LoVCoWg0IQkyaif+Kgjs\nAP3vl+3lp+Tz7h+ljr6JM43wS7Lhz1fkcmXx6fctPG5ZrutiCrca8bdC105TxdNeAZ7WNO03IcRo\n4/oQYBQQa3z0Bt43PisUCkXzc9lMyN4L+xbCtq9swzR73g47vpEuFp3e/vHWdwB5h+QoXgjbfT69\nAo5buZNuXQxO7rb7mHz6pnN2u+VvvZ2WoqkuHQ3wMi57A6aZkbHAbE2yAfARQoQ28bUUCoXCPjod\neIXJ5QX3wMldcvnuTdLNUl0qLwjWHN8M826VUTym0Xj81VBeUD+uv6bK1tiDrf/eREAsXDVL3oGc\nzwzgBmjqCP8BYIkQ4jXkxaOfsT0csLrvId3Ylln3BEKI6cB0gKioqLqbFQqFonH0vhO2fiGX07dA\n15sgME5GzoA02AGx4OAs1z8dLp+ry6QrKDQJhjwKexfA0VXg385y7pMptq8V2Rv0jvX7IAR0nyKj\ngv58VbqHhB6c3Jrznf5tzjjCF0IsE0LssvMYC9wFPKhpWiTwIPCp6TA7p7Kb+6xp2keapvXQNK1H\nYGDg330fCoXiUicoHsa8LpfL8y3+dL8YcPWDRQ/DK0Yjbi2LcGCxvBiE95BSzAALH4QsKyOfbhyt\nP7hbzh1M/f30fYnoCZoBfroLXghtuVyAs+SMBl/TtOGapnWx8/gZmAz8YNx1HtDLuJwORFqdJgKL\nu0ehUChaButJU5PLRQiLb72qWIZNzptS/1ivMFsf/9E/LcsZW8AjRMb8N6ofxtfb+4t8fr+fFFqr\nKJLrmgZL/3POBdea6sM/AQw2Lg8DTAGsC4BbjNE6fYBCTdPquXMUCoWiWQnuDA4u4OgGgfGWdh8r\nd/Fvj8D+X+Vy/wcs7d1vlc8mbZztX1tG+elb5AWk7kRuQ7j52a6XnJRSyju+kesFqbB2Fmz5vHHn\nayaaavBvB14XQvwFvIDRFw/8ChwBDgEfAzOa+DoKhUJxZvSO0KY/RA8AvdUUZewI+/t3GSefr/0I\n3P3lcqerIagzZO+BDwZA/lHIP2x/kvZ02MsKPpUmn01RQedYhqFJk7aapq0Buttp14C7m3JuhUKh\n+FuMn12/LW4U/PMgHFkFP0yTbff/Bb7Rst0jyHZ/nyjI3i2X306Wz+FnafBv+x2KT4Czl5R3AEs0\nkOk5Zy9UloCzx9md+2+iMm0VCsXFhbOHfQPqESRH9An/gKFPSGNvaq/LVbNsXUIgyyyeDY4ucsLY\nPUDeQUT1ldW6aqvlyN7BRU7snkM/vjL4CoXi0kGnh+s+gcGPnH4/z2C4u07cfVNG4UkToNd0qKmQ\nhVKydkLC9XJbxhYozACD4e+fv5Eog69QKBQN4dNGPj/RDBr3pjmArZ9DbRXEXi7vAI5vgnd7w5J/\nN/01zkBTE68UCoXi4uW2JTKixpSs1RS8I2U+wF/fyvWInnJeYM9P8gIQmtj01zgDaoSvUCgUDeEV\nCm36Ns+5hLAVZvMKk6P+2iq5fraTwn8DZfAVCoXiXNF9inx29ZXPJiPv7A3+7Vv85ZVLR6FQKM4V\nI1+EpIlS0wcgpAvonSC8mxSAa2GUwVcoFIpzhd7RNoHLwVleBAI6nJOXVwZfoVAozic9p52zl1I+\nfIVCobhEUAZfoVAoLhGUwVcoFIpLBGXwFQqF4hJBGXyFQqG4RFAGX6FQKC4RlMFXKBSKSwRl8BUK\nheISQcjiVK0DIUQOcOxvHh4AtI7S8OcO9Z4vDdR7vjRoyntuo2la4Jl2alUGvykIIbZomtbycnOt\nCPWeLw3Ue740OBfvWbl0FAqF4hJBGXyFQqG4RLiYDP5H57sD5wH1ni8N1Hu+NGjx93zR+PAVCoVC\ncXouphG+QqFQKE7DRWHwhRAjhRD7hRCHhBCPnu/+NBdCiEghxAohxF4hxG4hxP3Gdj8hxFIhxEHj\ns6+xXQgh3jZ+DjuFEN3O7zv4ewgh9EKI7UKIhcb1tkKIjcb3O1cI4WRsdzauHzJujz6f/W4KQggf\nIcT3Qoh9xu+778X8PQshHjT+pncJIb4VQrhcjN+zEOIzIUS2EGKXVdtZf69CiMnG/Q8KISb/3f5c\n8AZfCKEH3gVGAZ2AG4QQnc5vr5qNGuBhTdPigT7A3cb39iiwXNO0WGC5cR3kZxBrfEwH3j/3XW4W\n7gf2Wq2/DLxpfL8FwFRj+1SgQNO09sCbxv0uVGYBizVN6wgkId//Rfk9CyHCgfuAHpqmdQH0wEQu\nzu/5C2Bknbaz+l6FEH7AU0BvoBfwlOkicdZomnZBP4C+wBKr9ceAx853v1rovf4MjAD2A6HGtlBg\nv3H5Q+AGq/3N+10oDyDC+CcYBiwEBDIZxaHu9w0sAfoalx2M+4nz/R7+xnv2Ao7W7fvF+j0D4cBx\nwM/4vS0ErrhYv2cgGtj1d79X4AbgQ6t2m/3O5nHBj/Cx/HhMpBvbLiqMt7FdgY1AsKZpmQDG5yDj\nbhfDZ/EW8C/AYFz3B05pmlZjXLd+T+b3a9xeaNz/QiMGyAE+N7qyPhFCuHORfs+apmUArwFpQCby\ne9vKxf89mzjb77XZvu+LweALO20XVeiREMIDmA88oGla0el2tdN2wXwWQogrgWxN07ZaN9vZVWvE\ntgsJB6Ab8L6maf/fvv20RBWFcRz/PmAquSjbKQYpRNvaSbkQEhcubOMuSLRXEa58A9EbaBWRYIhI\nmxbWuj+LUKmokYQGsYKgRSsXT4vzXBukqJkuXebc3weGmXvuWZznPsMz95xz5xLwnZ/T/F/p6rhj\nOeIaMAoMAwOk5Yzjcsvzn/wuztLiz6HgN4GzLccjwH5FYymdmZ0gFfv77r4WzZ/MbCjODwGfo73b\nr8UVYNbM9oAV0rLOHeC0mfVEn9aYjuKN86eAr/9zwCVpAk13fxbHD0k/ALnmeQr44O5f3P0QWAMu\nk3+eC+3mtbR851DwXwDnY4e/l7T5s1HxmEphZgbcBd64++2WUxtAsVM/T1rbL9pvxG7/OPCtmDp2\nA3e/5e4j7n6OlMcn7n4deArMRbfj8RbXYS76d92dn7sfAB/N7EI0XQVek2meSUs542Z2Mr7jRbxZ\n57lFu3l9DEyb2WDMjqajrX1Vb2iUtCkyA7wDdoGlqsdTYlwTpKnbFvAqXjOk9ctN4H28n4n+Rnpi\naRfYJj0FUXkcHcY+CTyKz2PAc6ABrAJ90d4fx404P1b1uP8h3ovAy8j1OjCYc56BZeAtsAPcA/py\nzDPwgLRPcUi6U7/ZSV6BxYi/ASx0Oh7901ZEpCZyWNIREZG/oIIvIlITKvgiIjWhgi8iUhMq+CIi\nNaGCLyJSEyr4IiI1oYIvIlITPwBKTwGc5EVDFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_cum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.sum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
